A commentary on current practice in mediating variable analyses in behavioural nutrition and physical activity
|
|
- Roger Smith
- 5 years ago
- Views:
Transcription
1 Public Health Nutrition: 12(8), doi: /s A commentary on current practice in mediating variable analyses in behavioural nutrition and physical activity Ester Cerin 1, * and David P MacKinnon 2 1 Children s Nutrition Research Center, Baylor College of Medicine, 1100 Bates Street, Houston, TX 77030, USA: 2 Department of Psychology, Arizona State University, Tempe, AZ, USA Submitted 10 March 2008: Accepted 29 July 2008: First published online 9 September 2008 Abstract Objective: To critique current practice in, and provide recommendations for, mediating variable analyses (MVA) of nutrition and physical activity behaviour change. Strategy: Theory-based behavioural nutrition and physical activity interventions aim at changing mediating variables that are hypothesized to be responsible for changes in the outcome of interest. MVA are useful because they help to identify the most promising theoretical approaches, mediators and intervention components for behaviour change. However, the current literature suggests that MVA are often inappropriately conducted, poorly understood and inadequately presented. Main problems encountered in the published literature are explained and suggestions for overcoming weaknesses of current practice are proposed. Conclusion: The use of the most appropriate, currently available methods of MVA, and a correct, comprehensive presentation and interpretation of their findings, is of paramount importance for understanding how obesity can be treated and prevented. Keywords Mediation models Logistic regression Behaviour change There appears to be no better time to evaluate our current practices in analyses of mediators of dietary and physical activity behaviour than now. Following the modest performance of obesity prevention programmes (1) and in recognition of the importance of identifying the most effective mechanisms and strategies for change (2), the amount of research output on potential mediators of dietary and physical activity behaviour is increasing rapidly. The promise of analysis of mediating variables is the objective testing of theoretical processes in diet and physical activity prevention programmes. If these critical mediating processes can be identified, programmes may be improved by identifying important components and the cost of the programmes may be reduced by removing ineffective components. Ideally, true theoretical mediating mechanisms will emerge across studies and across behavioural interventions. Although mediation analysis is promising, before potentially going too far in delivering less-than-optimal communications, we need to evaluate the informational value and soundness of our current analytical practices and their interpretation. The aim of the current paper is to present general strategies that will help enhance the current quality of reports on mechanisms of obesity-related behaviour change. The paper starts with a brief overview of Baron and Kenny s (3) method of mediating variables analysis (MVA), then summarizes recent criticisms of their approach, and finally proposes ways to improve the presentation and interpretation of results from MVA in obesity-related behaviour research. Current practice: using the Baron and Kenny method Baron and Kenny s (3) seminal paper on mediators and moderators is by far the most cited published piece of work within studies investigating mediators of nutrition and physical activity behaviour. According to their not always correctly interpreted conceptual framework, to function as a mediator, a variable must meet four criteria. 1. An independent variable (e.g. fat-intake intervention) must be significantly associated with the potential mediator (e.g. change in self-efficacy for a low-fat diet); this relationship is operationalized as the a regression coefficient (also termed path coefficient in the language of structural equation modelling; see Fig. 1). 2. The potential mediator (self-efficacy) must be significantly associated with the outcome (e.g. change in fat intake), after adjustment for the independent variable; this is represented by the b coefficient (see Fig. 1). 3. After adjustment for the potential mediator (change in self-efficacy), a previously significant relationship between the outcome (change in fat intake) and the independent variable (fat-intake intervention) is no *Corresponding author: cerin@bcm.edu r The Authors 2008
2 Recommendations for mediation analyses 1183 Fat-intake intervention α τ Changes in self-efficacy for a low-fat diet Changes in fat intake β then influences the outcome (9). Consequently, our first take-home message is that to test mediation according to the Baron and Kenny method it is unnecessary to examine the unadjusted relationship between the mediator and the outcome. However, it is necessary to establish that the mediator is related to the outcome independently of the independent variable. Fat-intake intervention τ longer significant or is significantly attenuated; this is represented by the t 0 coefficient (see Fig. 1). 4. The third condition implies a significant relationship between the outcome (change in fat intake) and the independent variable (fat-intake intervention), which constitutes the fourth criterion; this relationship is represented by the t coefficient (see Fig. 1). All of these coefficients are best obtained using regression models or structural equation modelling. In the domain of behavioural prevention programmes, an evaluation of the first relationship (a coefficient) is termed action theory test and informs us on the extent to which we were successful in manipulating the hypothesized mechanisms of behaviour change. The second relationship (b coefficient) represents a conceptual theory test, i.e. the extent to which there is empirical support for our conceptual theory (e.g. Social Cognitive Theory) that changes in the hypothesized mechanisms cause changes in behaviour (4). The italicized text above highlights important aspects of MVA that are sometimes overlooked by researchers claiming to follow Baron and Kenny s method. Misinterpreting Baron and Kenny Changes in fat intake Fig. 1 Diagram for analysis of the effect of a fat-intake intervention on changes in fat intake mediated by self-efficacy for a low-fat diet It is not uncommon to see studies published in very respectable journals reporting a significant association between the mediator and the outcome, unadjusted for the effects of the independent variable, as one of the criteria for mediation (5 8). The source of confusion appears to be Baron and Kenny s unfortunate omission of the words controlling or adjusting for the independent variable in the first part of their well-cited article (p. 1176), while later (p. 1177) they clearly explain that the second criterion of mediation (see above) needs to be tested by regressing the dependent variable on both the independent variable and on the mediator (3). The reason for this necessary adjustment is that the mediator and the outcome may be related because they are both caused by the independent variable (in which case, there is no mediation) and not because the independent variable influences the mediator, which Beyond the four criteria of mediation: in search for a measure of mediated effect Although a formal statistical test of significance of the mediated effect (sometimes called indirect effect ) is not one of the explicit criteria of mediation proposed by Baron and Kenny, they nevertheless note that a significantly attenuated independent variable outcome relationship, following adjustment for the potential mediator, is a necessary condition for mediation. The magnitude of the attenuation is a measure of the mediated effect. The question is: what do we mean by significant mediated effect (or attenuation) and why is this information important? We argue that truly significant mediators are those that satisfy both requirements of statistical and substantive or clinical significance. While statistical significance ensures that we identify reliable mediators, substantive or clinical significance ensures that we focus our efforts on finding and manipulating mechanisms that produce the largest effects in terms of behaviour or health outcomes, thus maximizing the cost-effectiveness of our interventions. Here is where the vast majority of published work runs into problems. Let us start with statistical significance. There are many ways in which we can test the statistical significance of a mediated effect (10,11). In general, if a variable meets all four criteria of mediation as per Baron and Kenny, we can confidently conclude that it is a reliable mediator. In fact, simulation studies have shown Baron and Kenny s method to be one the most conservative tests of mediation (11,12). However, as explained later in more detail, the downside of this overly conservative approach is that, in some circumstances, it can fail to identify reliable and substantively meaningful mediators (13). On the other hand, large-scale studies may produce statistically significant but substantively or clinically trivial mediating effects. This is why it is important for studies adopting this particular MVA framework to go beyond the four criteria of mediation and explicitly report the observed mediated effect for each examined mediator. This needs to be done using simple, single-mediator as well as complex, multiple-mediator models evaluating the independent contribution of each mediating variable. Importantly, such practice is essential if solid knowledge about mechanism and strategies of behaviour change is to be swiftly accumulated and synthesized with the aid of meta-analytical procedures. Any alternative course of action is bound to unnecessarily slow down the progress in this area of research.
3 1184 E Cerin and DP Mackinnon A measure of a mediated effect can be obtained by computing the product of a coefficient (independentvariable effect on the mediator) and b coefficient (mediator effect on the outcome adjusted for the independent variable; see Fig. 1). This is termed product-of-coefficient estimate of a mediated effect (10 12). When using ordinary least squares regression in single-mediator models, it is algebraically equivalent to computing the reduction in the independent-variable effect after inclusion of the mediator in the regression model (10 12). This is named a differenceof-coefficients estimate. These estimates of mediated effect represent measures of the effect of the independent variable on the outcome via the mediator (the proposed mechanism) in the units used to measure the outcome (which implies that unstandardized regression coefficients have been used to compute them); hence their appeal and informative value. For instance, if an intervention produced a decrease in fat intake of 10 g/d, and after including self-efficacy for a low-fat diet in the model this effect is reduced to 4 g/d, we would state that the effect of the intervention mediated by self-efficacy was a decrease in fat intake of 6 g/d. In other words, the intervention yielded a reduction in fat intake of 6 g/d likely because of its effect on self-efficacy. In terms of product-of-coefficient estimate, if the intervention has an average effect on self-efficacy of 2 units on a 5-point scale (a coefficient) and the independent effect of self-efficacy is a decrease in fat of 3 g/d per unit increase in self-efficacy (b coefficient; conceptual theory test), by multiplying these estimates we obtain a mediated effect of the intervention through self-efficacy of 2 3 (23) g/d5 26 g/d (i.e. a decrease in fat intake of 6 g/d). These values inform us of the extent to which self-efficacy is important in changing the target behaviour (3 g/d per unit change in self-efficacy; conceptual theory test), our ability to change self-efficacy (a 2 unit change on a 5 point-scale; action theory test), and the expected benefit of targeting selfefficacy with a specific type of intervention within a specific target population (a decrease in fat intake of 6 g/d). Hence, the second take-home message of the present paper, especially relevant to intervention studies, is that it is important to report estimates of mediated effects as well as action and conceptual theory tests (see Haerens et al. (14) for an example). Beyond the four criteria of mediation: a special note on the dichotomous case Dichotomous outcome variables, such as being overweight v. being of normal weight or meeting v. not meeting the physical activity recommendations for general health, are often used in behavioural nutrition and physical activity research. In such cases, assuming that the mediator is a continuous variable, a mediating effect can be computed using a product-of-coefficient test based on the estimate of a from ordinary least squares regression and b from logistic or probit regression (10,11). This method has been found to be robust against departures from the logistic or probit assumptions as well as normality assumptions for the distribution of the mediator (15). Importantly, for dichotomous outcomes, the differenceof-coefficient method yields incorrect results and, hence, its use is not recommended (11,15). The same applies to the commonly used approach to MVA based on an examination of the presence of a reduction in the regression coefficient of the independent variable (e.g. intervention) after inclusion of the mediator in the logistic regression model (criterion 3 of the Baron Kenny method). Specifically, when using these methods, larger actual mediated effects may paradoxically result in smaller or nil estimates of the mediated effect or a reduction in coefficients. This effect is due to the difference in scales of the logistic regression coefficients of the outcome on the independent variable adjusting and not adjusting for the mediator, since these coefficients are derived from separate logistic regression equations (with a fixed error variance). Hence, the potential negative consequences of using Baron and Kenny s method to conduct MVA with a dichotomous outcome are particularly serious. The third take-home message of the paper is to use the product-of-coefficient method when dealing with dichotomous outcome variables. Beyond the four criteria of mediation: confidence intervals Any statistical estimate of effect is accompanied by a measure of its accuracy (i.e. a standard error), which allows for the computation of the range of plausible values (i.e. the confidence interval) of the population parameter (i.e. mediated effect) supported by the data. Confidence intervals tell us how confident we can be that the observed estimate of effect corresponds to the effect in the target population. They provide clearer information than the usual P values because the latter are a confounded mixture of effect size and sample size, while the width of the former is determined by sample size and variability in effect of interest (determining accuracy). This also applies to the estimate of the mediated effect. Using the fat-intake example, a 95 % confidence interval of fat intake ranging from 21 to 211 g/d would indicate that the plausible population effects of the intervention on fat intake through self-efficacy are a decrease of fat intake from 1 to 11 g/d. The information given by the point estimate and confidence limits of the mediated effect is very useful in guiding efforts to enhance theory and practice of behaviour change. For instance, a clinically meaningful mediated effect accompanied by large confidence limits indicates four main possible sources of problems: (i) insufficient sample size; (ii) large measurement error; (iii) collinearity between the independent variable and the mediator affecting the accuracy of the b coefficient estimate (3) ; (iv) large interindividual variations in the effects of the intervention on the
4 Recommendations for mediation analyses 1185 mediator or/and the effects of the mediator on the outcome. In contrast, a clinically trivial but accurate (with a narrow confidence interval) estimate of mediated effect points directly at problems with action (the effectiveness of our intervention in changing the mediator) and conceptual theory (theoretical mechanisms of change). Thus our fourth take-home message is: report confidence intervals of mediated effects and interpret them conjointly with their point estimates. How do we obtain confidence limits of mediated effects? Baron and Kenny (3) provided a formula for the standard error of the mediated effect based on Sobel (16). The use of the formula assumes that the mediated effect is approximately normally distributed. This solution is less than optimal because, unless the ratio of the a and/or b coefficients to their standard errors is equal or greater than 6, mediated effects are not normally distributed (12,17 19). However, it is very easy to implement by those less versed in more sophisticated methods of MVA. All it requires are the point estimates and standard errors of a and b coefficients (see Preacher s (2003) online program at and associated warnings ). More optimal and recommended solutions involve the computation of asymmetric confidence intervals of mediated effects using a recent program developed by MacKinnon et al. (20) (which can be found at davidpm/ripl/prodclin) and the use of bootstrapping resampling techniques (17,19,21). Bootstrapping allows the distribution of the mediated effect to be estimated empirically by using information from the original sample (treated as a pseudo population) (22). It is particularly recommended for non-normally distributed variables, such as mediated effects, for which parametric inferential statistics may produce biased estimates. This method consists in drawing with replacement a large number of bootstrap samples (e.g. 1000) from the original sample; estimating mediated effects for each of these samples; averaging the effect estimates across all of the bootstrap samples; and computing the 95 % percentile confidence intervals for the mediated effects across all of the bootstrap samples (22). Mediators whose confidence limits do not include zero are considered reliable (statistically significant). It is also recommended that the more accurate bias-corrected bootstrap confidence intervals, corrected for the difference between the original estimate and the bootstrap mean estimate, be computed (23) (see Cerin and Leslie (24) for examples in the field of physical activity). Is it really necessary to test the association between the independent variable and the outcome in mediating variable analysis? The ultimate aim of MVA is to establish whether an independent variable influences the outcome through its effect on a mediating variable. If this is the case, do we really need to show that the independent variable is related to the outcome (fourth criterion of mediation as per Baron and Kenny)? The answer is no. We might be legitimately interested in the overall association of, for instance, socio-economic status and physical activity behaviour. However, a significant association between the two is not a requirement for mediation to occur. Actually, this particular criterion can hinder the discovery of substantive mechanisms of influence. There are three major reasons for this. The first reason, discussed by Judd and Kenny (25) and more recently explained by Kraemer et al. (26), relates to the fact that the effect of the mediator on the outcome may depend on the values of the independent variable (interaction effect). It is theoretically possible for an intervention to leave unaltered the average level of the outcome (t coefficient) and mediator (b coefficient) but change the direction of the effect of the mediator on the outcome (see Fig. 2a; b C and b T coefficients). In such a case, mediation would occur without observing a significant intervention effect on the outcome. As a matter of fact, there might not even be any significant intervention effects on the mediator or overall independent effect of the mediator on the outcome (Fig. 2a). The second reason regards the possibility that the effect of an independent variable on the outcome be explained by multiple competing mechanisms whose influences are of opposite direction and cancel out (19,27). For instance, it has been shown that an intervention-induced increase in physical activity (mediator of intervention effect, if the aim is to reduce BMI by increasing energy expenditure) in normal-weight adults does not generally result in a decrease in BMI (outcome) because it is also accompanied by an increase in energy intake (counterproductive or inconsistent mediator of intervention effect) (28).Theintervention triggers two different mechanisms exerting opposite effects on BMI. It is clear that here mediation occurs even in lack of a significant overall effect of the intervention on the outcome (see Fig. 2b). Inconsistent mediation, also called suppression, can sometimes be counterintuitive (see MacKinnon et al. (27) for a discussion on inconsistent mediators). In such cases, assuming a robust effect, it is a warning that there may be serious problems with: (i) our action and conceptual theories; or (ii) our mediating variables measures. For example, unexpected inconsistent mediation effects have been recently reported in physical activity and nutrition intervention studies (14,29). Self-efficacy and perceived barriers were found to be independently associated, in the theoretically predicted direction, with changes in physical activity and fat intake, respectively. However, the intervention was found to have a negative effect on selfefficacy and perceived barriers, resulting in inconsistent mediation effects. In other words, the intervention would have been more successful had not it exerted a negative
5 1186 E Cerin and DP Mackinnon (a) Intervention τ =0 Changes in mediator α =0 β =0 Intervention τ =0 Control group Changes in mediator β =10 C Treatment group Changes in mediator β = 10 T (b) Physical activity (PA) intervention τ =0 Changes in BMI Changes in PA α =10 min/d β = 0 03 kg/m PA intervention τ =0 Changes in BMI α2=55 kcal/d Changes in energy β = kg/m 2 2 intake α1 β 1 = 0 30 kg/m 2 (intervention effects mediated by changes in PA) α2 β 2 =0 30 kg/m 2 (intervention effects mediated by changes in energy intake) Fig. 2 Examples of mediation in the absence of a significant association between the independent variable and the outcome: (a) interaction between an intervention and the mediator; (b) inconsistent mediation effect on self-efficacy and perceived barriers as mechanisms of behaviour change. Changes in self-efficacy and perceived barriers suppressed the intervention effectiveness. The authors concluded that their strategies to manipulate mediators of behaviour change in adolescents needed to be reconsidered (problems with action theory) and/or that an intervention by measurement effect might have occurred, whereby exposure to the intervention affected
6 Recommendations for mediation analyses 1187 the interpretation of the items in relation to the underlying construct (change of location of the items on the underlying construct). These explanations can be verified by employing differential item functioning (DIF) techniques in combination with having objective behavioural markers of self-efficacy and perceived barriers to contrast with the DIF of the scale items (30). The third reason for not considering a significant relationship between an independent variable and the outcome as a necessary criterion of mediation is low statistical power (12,18,19). This argument is particularly relevant if we believe that the effect of the independent variable on the outcome is temporally or causally distal (19). In their simulation studies, Fritz and MacKinnon (18) have shown that, under conditions of complete mediation and small effect size, we would need a sample size of approximately cases to achieve acceptable levels of statistical power. In contrast, the sample size required to detect the same mediated effect using product-ofcoefficient tests (e.g. Sobel test, bootstrap estimates and asymmetric confidence intervals) would range between 490 and 660 cases. Conclusion To recap, the main points to remember in improving our current practices in MVA are: 1. Examine potential mediating effects even in the absence of a significant association between the independent and outcome variables. 2. Examine whether there is an interaction effect of the independent variable and the mediator on the outcome (applicable to prospective studies). 3. Report point estimates of the mediated effects, preferably in the units of the outcome measure. 4. Report confidence intervals of the mediated effects, preferably using accurate computational methods (asymmetric confidence intervals, bootstrapping). 5. When examining multiple mediators, aim at evaluating the independent contribution of each of them using complex, multiple-mediator models. 6. In intervention studies, test action (the extent to which the intervention affected the mediator) and conceptual theories (the extent to which changes in the mediator likely caused changes in the outcome). 7. Do not use Baron and Kenny s method and the difference-of-coefficients method of MVA with dichotomous outcome variables. Technical, methodological and conceptual issues related to MVA are complex and cannot be satisfactorily discussed in a single paper. Our aim was to highlight a few fundamental issues and strategies related to current practice in MVA in the research fields of behavioural nutrition and physical activity that is hoped will help us improve our understanding of how we can combat the obesity problem in our societies. Acknowledgement Conflicts of interest: There are no conflicts of interest. Authorship responsibilities: E.C. conceptualized and wrote the manuscript. D.P.M. critically reviewed and approved the manuscript. References 1. Flynn MA, McNeil DA, Maloff B, Mutasingwa D, Wu M, Ford C & Tough SC (2006) Reducing obesity and related chronic disease risk in children and youth: a synthesis of evidence with best practice recommendations. Obes Rev 7, Suppl. 1, Baranowski T, Anderson C & Carmack C (1998) Mediating variable framework in physical activity interventions: how are we doing? How might we do better? Am J Prev Med 15, Baron RM & Kenny DA (1986) The moderator mediator distinction in social psychological research: conceptual, strategic, and statistical considerations. J Pers Soc Psychol 51, Chen TH (1990) Theory-Driven Evaluations. Newbury Park, CA: Sage. 5. Miller Y, Trost S & Brown W (2002) Mediators of physical activity behavior among women with young children. Am J Prev Med 23, 2 Suppl., Moore GF, Tapper K, Murphy S, Lynch R, Raisanen L, Pimm C & Moore L (2007) Associations between deprivation, attitudes towards eating breakfast and breakfast eating behaviours in 9 11-year-olds. Public Health Nutr 10, Pinto BM, Lynn H, Marcus BH, DePue J & Goldstein MG (2001) Physician-based activity counseling: intervention effects on mediators of motivational readiness for physical activity. Ann Behav Med 23, Prodaniuk TR, Plotnikoff RC, Spence JC & Wilson PM (2004) The influence of self-efficacy and outcome expectations on the relationship between perceived environment and physical activity in the workplace. Int J Behav Nutr Phys Act 1, Kenny DA, Kashy DA & Bolger N (1998) Data analysis in social psychology. In The Handbook of Social Psychology, vol. 1, pp [DT Gilbert, ST Fiske and G Lindzey, editors]. New York: Oxford University Press. 10. MacKinnon DP (2008) Introduction to Statistical Mediation Analysis. Mahwah, NJ: Erlbaum. 11. MacKinnon DP, Fairchild AJ & Fritz MS (2007) Mediation analysis. Annu Rev Psychol 58, MacKinnon DP, Lockwood CM, Hoffman JM, West SG & Sheet V (2002) A comparison of methods to test mediation and other intervening variable effects. Psychol Methods 7, Cerin E, Taylor LM, Leslie E & Owen N (2006) Small-scale randomized controlled trials need more powerful methods of mediational analysis than the Baron Kenny method. J Clin Epidemiol 59, Haerens L, Cerin E, Maes L, Cardon G, Deforche B & De Bourdeaudhuij I (2008) Explaining the effect of a 1-year intervention promoting physical activity in middle schools: a mediation analysis. Public Health Nutr 11, MacKinnon DP, Lockwood CM, Brown CH, Wang W & Hoffman JM (2007) The intermediate endpoint effect in logistic and probit regression. Clin Trials 4,
7 1188 E Cerin and DP Mackinnon 16. Sobel ME (1982) Asymptotic confidence intervals for indirect effects in structural equation models. In Sociological Methodology, pp [S Leinhardt, editor]. Washington, DC: American Sociological Association. 17. Bollen KA & Stine R (1990) Direct and indirect effects: classical and bootstrap estimates of variability. Sociol Methodol 20, Fritz MS & MacKinnon DP (2007) Required sample size to detect the mediated effect. Psychol Sci 18, Shrout PE & Bolger N (2002) Mediation in experimental and nonexperimental studies: new procedures and recommendations. Psychol Methods 7, MacKinnon DP, Fritz MS, Williams J & Lockwood CM (2007) Distribution of the product confidence limits for the indirect effect: program PRODCLIN. Behav Res Methods 39, Preacher KJ & Hayes AF (2004) SPSS and SAS procedures for estimating indirect effects in simple mediation models. Behav Res Methods Instrum Comput 36, Efron B & Tibshirani TJ (1993) An Introduction to the Bootstrap. New York: Chapman & Hall. 23. Pituch KA, Stapleton LM & Kang JY (2006) A comparison for single sample and bootstrap methods to assess mediation in cluster randomized trials. Multivariate Behav Res 41, Cerin E & Leslie E (2008) How socio-economic status contributes to participation in leisure-time physical activity. Soc Sci Med 66, Judd CM & Kenny DA (1981) Process analysis: estimating mediation in treatment evaluations. Eval Rev 5, Kraemer H, Wilson G, Fairburn C & Agras W (2002) Mediators and moderators of treatment effects in randomized clinical trials. Arch Gen Psychiatry 59, MacKinnon DP, Krull JL & Lockwood CM (2000) Equivalence of mediation, confounding and suppression effects. Prev Sci 1, Meltzer K, Kayser B, Saris WHM & Pichard C (2005) Effects of physical activity on food intake. Clin Nutr 24, Haerens L, Cerin E, Deforche B, Maes L & De Bourdeaudhuij I (2007) Explaining the effects of a 1-year intervention promoting a low fat diet in adolescent girls: a mediation analysis. Int J Behav Nutr Phys Act 4, Baranowski T, Allen DD, Masse LC & Wilson M (2006) Does participation in an intervention affect responses on self-report questionnaires? Health Educ Res 21,
Current Directions in Mediation Analysis David P. MacKinnon 1 and Amanda J. Fairchild 2
CURRENT DIRECTIONS IN PSYCHOLOGICAL SCIENCE Current Directions in Mediation Analysis David P. MacKinnon 1 and Amanda J. Fairchild 2 1 Arizona State University and 2 University of South Carolina ABSTRACT
More informationTHE INDIRECT EFFECT IN MULTIPLE MEDIATORS MODEL BY STRUCTURAL EQUATION MODELING ABSTRACT
European Journal of Business, Economics and Accountancy Vol. 4, No. 3, 016 ISSN 056-6018 THE INDIRECT EFFECT IN MULTIPLE MEDIATORS MODEL BY STRUCTURAL EQUATION MODELING Li-Ju Chen Department of Business
More informationStrategies to Measure Direct and Indirect Effects in Multi-mediator Models. Paloma Bernal Turnes
China-USA Business Review, October 2015, Vol. 14, No. 10, 504-514 doi: 10.17265/1537-1514/2015.10.003 D DAVID PUBLISHING Strategies to Measure Direct and Indirect Effects in Multi-mediator Models Paloma
More informationHow to Model Mediating and Moderating Effects. Hsueh-Sheng Wu CFDR Workshop Series November 3, 2014
How to Model Mediating and Moderating Effects Hsueh-Sheng Wu CFDR Workshop Series November 3, 2014 1 Outline Why study moderation and/or mediation? Moderation Logic Testing moderation in regression Checklist
More informationSPSS Learning Objectives:
Tasks Two lab reports Three homeworks Shared file of possible questions Three annotated bibliographies Interviews with stakeholders 1 SPSS Learning Objectives: Be able to get means, SDs, frequencies and
More informationNIH Public Access Author Manuscript Res Soc Work Pract. Author manuscript; available in PMC 2012 June 04.
NIH Public Access Author Manuscript Published in final edited form as: Res Soc Work Pract. 2011 November ; 21(6): 675 681. doi:10.1177/1049731511414148. Integrating Mediators and Moderators in Research
More informationW e l e a d
http://www.ramayah.com 1 2 Developing a Robust Research Framework T. Ramayah School of Management Universiti Sains Malaysia ramayah@usm.my Variables in Research Moderator Independent Mediator Dependent
More informationRUNNING HEAD: TESTING INDIRECT EFFECTS 1 **** TO APPEAR IN JOURNAL OF PERSONALITY AND SOCIAL PSYCHOLOGY ****
RUNNING HEAD: TESTING INDIRECT EFFECTS 1 **** TO APPEAR IN JOURNAL OF PERSONALITY AND SOCIAL PSYCHOLOGY **** New recommendations for testing indirect effects in mediational models: The need to report and
More informationChapter 7 : Mediation Analysis and Hypotheses Testing
Chapter 7 : Mediation Analysis and Hypotheses ing 7.1. Introduction Data analysis of the mediating hypotheses testing will investigate the impact of mediator on the relationship between independent variables
More informationUsing Mediated Moderation to Explain Treatment-by-Site Interaction in Multisite Clinical Trials
Using Mediated Moderation to Explain Treatment-by-Site Interaction in Multisite Clinical Trials Assistant Professor of Medicine, Biostatistics, and Clinical & Translational Sciences Center for Research
More informationWhat works in school based energy balance behavior interventions and what does not? A systematic review of mediating mechanisms
4 What works in school based energy balance behavior interventions and what does not? A systematic review of mediating mechanisms Maartje M van Stralen, Mine Yıldırım, Saskia J te Velde, Johannes Brug,
More informationFull terms and conditions of use:
This article was downloaded by: [University of Massachusetts, Amherst] On: 14 December 2012, At: 09:57 Publisher: Routledge Informa Ltd Registered in England and Wales Registered Number: 1072954 Registered
More informationThe Logic of Causal Inference
The Logic of Causal Inference Judea Pearl University of California, Los Angeles Computer Science Department Los Angeles, CA, 90095-1596, USA judea@cs.ucla.edu September 13, 2010 1 Introduction The role
More informationMediation Testing in Management Research: A Review and Proposals
Utah State University From the SelectedWorks of Alison Cook January 1, 2008 Mediation Testing in Management Research: A Review and Proposals R. E. Wood J. S. Goodman N. Beckmann Alison Cook, Utah State
More informationHow to Model Mediating and Moderating Effects. Hsueh-Sheng Wu CFDR Workshop Series Summer 2012
How to Model Mediating and Moderating Effects Hsueh-Sheng Wu CFDR Workshop Series Summer 2012 1 Outline Why study moderation and/or mediation? Moderation Logic Testing moderation in regression Checklist
More informationENDOGENEITY: AN OVERLOOKED THREAT TO VALIDITY OF CROSS-SECTIONAL RESEARCH. John Antonakis
ENDOGENEITY: AN OVERLOOKED THREAT TO VALIDITY OF CROSS-SECTIONAL RESEARCH John Antonakis Professor of Organizational Behavior Faculty of Business and Economics University of Lausanne Research seminar 28
More informationDemonstrating multilevel structural equation modeling for testing mediation: Effects of self-critical perfectionism on daily affect
Demonstrating multilevel structural equation modeling for testing mediation: Effects of self-critical perfectionism on daily affect Kristopher J. Preacher University of Kansas David M. Dunkley and David
More informationPLS 506 Mark T. Imperial, Ph.D. Lecture Notes: Reliability & Validity
PLS 506 Mark T. Imperial, Ph.D. Lecture Notes: Reliability & Validity Measurement & Variables - Initial step is to conceptualize and clarify the concepts embedded in a hypothesis or research question with
More informationMultiple Mediation Analysis For General Models -with Application to Explore Racial Disparity in Breast Cancer Survival Analysis
Multiple For General Models -with Application to Explore Racial Disparity in Breast Cancer Survival Qingzhao Yu Joint Work with Ms. Ying Fan and Dr. Xiaocheng Wu Louisiana Tumor Registry, LSUHSC June 5th,
More informationCHAPTER III RESEARCH METHODOLOGY
CHAPTER III RESEARCH METHODOLOGY In this chapter, the researcher will elaborate the methodology of the measurements. This chapter emphasize about the research methodology, data source, population and sampling,
More informationWhen ab c c : Published errors in the reports of single-mediator models
Behav Res (2013) 45:595 601 DOI 10.3758/s13428-012-0262-5 When ab c c : Published errors in the reports of single-mediator models John V. Petrocelli & Joshua J. Clarkson & Melanie B. Whitmire & Paul E.
More informationAlternative Methods for Assessing the Fit of Structural Equation Models in Developmental Research
Alternative Methods for Assessing the Fit of Structural Equation Models in Developmental Research Michael T. Willoughby, B.S. & Patrick J. Curran, Ph.D. Duke University Abstract Structural Equation Modeling
More informationCHAMP: CHecklist for the Appraisal of Moderators and Predictors
CHAMP - Page 1 of 13 CHAMP: CHecklist for the Appraisal of Moderators and Predictors About the checklist In this document, a CHecklist for the Appraisal of Moderators and Predictors (CHAMP) is presented.
More informationTHE LINK BETWEEN HUMAN DEVELOPMENT AND TAX COMPLIANCE: EVIDENCE FROM A MEDIATION ANALYSIS
THE LINK BETWEEN HUMAN DEVELOPMENT AND TAX COMPLIANCE: EVIDENCE FROM A MEDIATION ANALYSIS BǍTRÂNCEA LARISSA ASSISTANT PROFESSOR PHD, BABEŞ-BOLYAI UNIVERSITY, FACULTY OF BUSINESS, CLUJ-NAPOCA, ROMANIA,
More informationDurham Research Online
Durham Research Online Deposited in DRO: 15 April 2015 Version of attached le: Accepted Version Peer-review status of attached le: Peer-reviewed Citation for published item: Wood, R.E. and Goodman, J.S.
More informationCross-Lagged Panel Analysis
Cross-Lagged Panel Analysis Michael W. Kearney Cross-lagged panel analysis is an analytical strategy used to describe reciprocal relationships, or directional influences, between variables over time. Cross-lagged
More informationModelling Double-Moderated-Mediation & Confounder Effects Using Bayesian Statistics
Modelling Double-Moderated-Mediation & Confounder Effects Using Bayesian Statistics George Chryssochoidis*, Lars Tummers**, Rens van de Schoot***, * Norwich Business School, University of East Anglia **
More informationMediation Analysis. David P. MacKinnon, Amanda J. Fairchild, and Matthew S. Fritz. Key Words. Abstract
Annu. Rev. Psychol. 2007. 58:593 614 First published online as a Review in Advance on August 29, 2006 The Annual Review of Psychology is online at http://psych.annualreviews.org This article s doi: 10.1146/annurev.psych.58.110405.085542
More informationCochrane Pregnancy and Childbirth Group Methodological Guidelines
Cochrane Pregnancy and Childbirth Group Methodological Guidelines [Prepared by Simon Gates: July 2009, updated July 2012] These guidelines are intended to aid quality and consistency across the reviews
More informationTitle: Mediators of the association between parental education and breakfast consumption among adolescents in Norway: the ESSENS study
Author s response to reviews Title: Mediators of the association between parental education and breakfast consumption among adolescents in Norway: the ESSENS study Authors: Mekdes Gebremariam (mekdes.gebremariam@medisin.uio.no;mekdes-kebede.gebremariam@-
More informationEquivalence of the Mediation, Confounding and Suppression Effect
Prevention Science, Vol. 1, No. 4, 2000 Equivalence of the Mediation, Confounding and Suppression Effect David P. MacKinnon, 1,2 Jennifer L. Krull, 1 and Chondra M. Lockwood 1 This paper describes the
More informationCausal Mediation Analysis with the CAUSALMED Procedure
Paper SAS1991-2018 Causal Mediation Analysis with the CAUSALMED Procedure Yiu-Fai Yung, Michael Lamm, and Wei Zhang, SAS Institute Inc. Abstract Important policy and health care decisions often depend
More informationDesign and Analysis Plan Quantitative Synthesis of Federally-Funded Teen Pregnancy Prevention Programs HHS Contract #HHSP I 5/2/2016
Design and Analysis Plan Quantitative Synthesis of Federally-Funded Teen Pregnancy Prevention Programs HHS Contract #HHSP233201500069I 5/2/2016 Overview The goal of the meta-analysis is to assess the effects
More informationReduction in sugar-sweetened beverages is not associated with more water or diet drinks
Reduction in sugar-sweetened beverages is not associated with more water or diet drinks Citation: Veitch, Jenny, Singh, Amika, van Stralen, Maartje, Van Mechelen, Willem, Brug, Johannes and ChinAPaw, Mai
More informationBayesian Mediation Analysis
Psychological Methods 2009, Vol. 14, No. 4, 301 322 2009 American Psychological Association 1082-989X/09/$12.00 DOI: 10.1037/a0016972 Bayesian Mediation Analysis Ying Yuan The University of Texas M. D.
More informationAnalysis of the Reliability and Validity of an Edgenuity Algebra I Quiz
Analysis of the Reliability and Validity of an Edgenuity Algebra I Quiz This study presents the steps Edgenuity uses to evaluate the reliability and validity of its quizzes, topic tests, and cumulative
More informationConceptual, Methodological, and Sta3s3cal Dis3nc3ons between Modera3on and Media3on
CYFS Research Methodology Series Conceptual, Methodological, and Sta3s3cal Dis3nc3ons between Modera3on and Media3on Kyongboon Kwon, Ph.D. Postdoctoral Fellow Center for Research on Children, Youth, Families,
More informationA brief history of the Fail Safe Number in Applied Research. Moritz Heene. University of Graz, Austria
History of the Fail Safe Number 1 A brief history of the Fail Safe Number in Applied Research Moritz Heene University of Graz, Austria History of the Fail Safe Number 2 Introduction Rosenthal s (1979)
More informationConfidence Intervals On Subsets May Be Misleading
Journal of Modern Applied Statistical Methods Volume 3 Issue 2 Article 2 11-1-2004 Confidence Intervals On Subsets May Be Misleading Juliet Popper Shaffer University of California, Berkeley, shaffer@stat.berkeley.edu
More informationCitation for published version (APA): Ebbes, P. (2004). Latent instrumental variables: a new approach to solve for endogeneity s.n.
University of Groningen Latent instrumental variables Ebbes, P. IMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite from it. Please check the document
More informationScore Tests of Normality in Bivariate Probit Models
Score Tests of Normality in Bivariate Probit Models Anthony Murphy Nuffield College, Oxford OX1 1NF, UK Abstract: A relatively simple and convenient score test of normality in the bivariate probit model
More informationinvestigate. educate. inform.
investigate. educate. inform. Research Design What drives your research design? The battle between Qualitative and Quantitative is over Think before you leap What SHOULD drive your research design. Advanced
More informationSUPPLEMENTARY INFORMATION
In the format provided by the authors and unedited. SUPPLEMENTARY INFORMATION VOLUME: 1 ARTICLE NUMBER: 0056 Online Supplement On the benefits of explaining herd immunity in vaccine advocacy Cornelia Betsch
More informationAssignment 4: True or Quasi-Experiment
Assignment 4: True or Quasi-Experiment Objectives: After completing this assignment, you will be able to Evaluate when you must use an experiment to answer a research question Develop statistical hypotheses
More informationAlan S. Gerber Donald P. Green Yale University. January 4, 2003
Technical Note on the Conditions Under Which It is Efficient to Discard Observations Assigned to Multiple Treatments in an Experiment Using a Factorial Design Alan S. Gerber Donald P. Green Yale University
More informationSupplementary Materials. Worksite Wellness Programs: Sticks (Not Carrots) Send Stigmatizing Signals
Supplementary Materials Worksite Wellness Programs: Sticks (Not Carrots) Send Stigmatizing Signals David Tannenbaum, Chad J. Valasek, Eric D. Knowles, Peter H. Ditto Additional Mediation Analyses for Study
More informationBest (but oft-forgotten) practices: mediation analysis 1,2
Statistical Commentary Best (but oft-forgotten) practices: mediation analysis 1,2 Amanda J Fairchild* and Heather L McDaniel Department of Psychology, University of South Carolina, Columbia, SC ABSTRACT
More informationSense-making Approach in Determining Health Situation, Information Seeking and Usage
DOI: 10.7763/IPEDR. 2013. V62. 16 Sense-making Approach in Determining Health Situation, Information Seeking and Usage Ismail Sualman 1 and Rosni Jaafar 1 Faculty of Communication and Media Studies, Universiti
More informationRunning Head: BAYESIAN MEDIATION WITH MISSING DATA 1. A Bayesian Approach for Estimating Mediation Effects with Missing Data. Craig K.
Running Head: BAYESIAN MEDIATION WITH MISSING DATA 1 A Bayesian Approach for Estimating Mediation Effects with Missing Data Craig K. Enders Arizona State University Amanda J. Fairchild University of South
More informationHow do contextual factors influence causal processes? Conditional Process Models
How do contextual factors influence causal processes? Conditional Process Models Amanda J. Fairchild, PhD Department of Psychology September 4 th, 2014 This work was supported in part by the National Institute
More informationTechnical Specifications
Technical Specifications In order to provide summary information across a set of exercises, all tests must employ some form of scoring models. The most familiar of these scoring models is the one typically
More informationMediational Analysis in HIV/AIDS Research: Estimating Multivariate Path Analytic Models in a Structural Equation Modeling Framework
AIDS Behav (2007) 11:365 383 DOI 10.1007/s10461-006-9150-2 REVIEW PAPER Mediational Analysis in HIV/AIDS Research: Estimating Multivariate Path Analytic Models in a Structural Equation Modeling Framework
More informationHerman Aguinis Avram Tucker Distinguished Scholar George Washington U. School of Business
Herman Aguinis Avram Tucker Distinguished Scholar George Washington U. School of Business The Urgency of Methods Training Research methodology is now more important than ever Retractions, ethical violations,
More informationEc331: Research in Applied Economics Spring term, Panel Data: brief outlines
Ec331: Research in Applied Economics Spring term, 2014 Panel Data: brief outlines Remaining structure Final Presentations (5%) Fridays, 9-10 in H3.45. 15 mins, 8 slides maximum Wk.6 Labour Supply - Wilfred
More informationDRAFT (Final) Concept Paper On choosing appropriate estimands and defining sensitivity analyses in confirmatory clinical trials
DRAFT (Final) Concept Paper On choosing appropriate estimands and defining sensitivity analyses in confirmatory clinical trials EFSPI Comments Page General Priority (H/M/L) Comment The concept to develop
More informationThe Role of Self-discipline in Predicting Achievement for 10th Graders
International Journal of Intelligent Technologies and Applied Statistics Vol.8, No.1 (2015) pp.61-70, DOI: 10.6148/IJITAS.2015.0801.05 Airiti Press The Role of Self-discipline in Predicting for 10th Graders
More informationAn Empirical Study on Causal Relationships between Perceived Enjoyment and Perceived Ease of Use
An Empirical Study on Causal Relationships between Perceived Enjoyment and Perceived Ease of Use Heshan Sun Syracuse University hesun@syr.edu Ping Zhang Syracuse University pzhang@syr.edu ABSTRACT Causality
More informationAudio: In this lecture we are going to address psychology as a science. Slide #2
Psychology 312: Lecture 2 Psychology as a Science Slide #1 Psychology As A Science In this lecture we are going to address psychology as a science. Slide #2 Outline Psychology is an empirical science.
More informationDoes momentary accessibility influence metacomprehension judgments? The influence of study judgment lags on accessibility effects
Psychonomic Bulletin & Review 26, 13 (1), 6-65 Does momentary accessibility influence metacomprehension judgments? The influence of study judgment lags on accessibility effects JULIE M. C. BAKER and JOHN
More informationVERDIN MANUSCRIPT REVIEW HISTORY REVISION NOTES FROM AUTHORS (ROUND 2)
1 VERDIN MANUSCRIPT REVIEW HISTORY REVISION NOTES FROM AUTHORS (ROUND 2) Thank you for providing us with the opportunity to revise our paper. We have revised the manuscript according to the editors and
More informationSupplement 2. Use of Directed Acyclic Graphs (DAGs)
Supplement 2. Use of Directed Acyclic Graphs (DAGs) Abstract This supplement describes how counterfactual theory is used to define causal effects and the conditions in which observed data can be used to
More informationLive WebEx meeting agenda
10:00am 10:30am Using OpenMeta[Analyst] to extract quantitative data from published literature Live WebEx meeting agenda August 25, 10:00am-12:00pm ET 10:30am 11:20am Lecture (this will be recorded) 11:20am
More informationModeling the Influential Factors of 8 th Grades Student s Mathematics Achievement in Malaysia by Using Structural Equation Modeling (SEM)
International Journal of Advances in Applied Sciences (IJAAS) Vol. 3, No. 4, December 2014, pp. 172~177 ISSN: 2252-8814 172 Modeling the Influential Factors of 8 th Grades Student s Mathematics Achievement
More informationAddendum: Multiple Regression Analysis (DRAFT 8/2/07)
Addendum: Multiple Regression Analysis (DRAFT 8/2/07) When conducting a rapid ethnographic assessment, program staff may: Want to assess the relative degree to which a number of possible predictive variables
More informationIs Leisure Theory Needed For Leisure Studies?
Journal of Leisure Research Copyright 2000 2000, Vol. 32, No. 1, pp. 138-142 National Recreation and Park Association Is Leisure Theory Needed For Leisure Studies? KEYWORDS: Mark S. Searle College of Human
More informationThe Cost-Effectiveness of Individual Cognitive Behaviour Therapy for Overweight / Obese Adolescents
Dr Marion HAAS R Norman 1, J Walkley 2, L Brennan 2, M Haas 1. 1 Centre for Health Economics Research and Evaluation, University of Technology, Sydney. 2 School of Medical Sciences, RMIT University, Melbourne.
More informationPERCEIVED TRUSTWORTHINESS OF KNOWLEDGE SOURCES: THE MODERATING IMPACT OF RELATIONSHIP LENGTH
PERCEIVED TRUSTWORTHINESS OF KNOWLEDGE SOURCES: THE MODERATING IMPACT OF RELATIONSHIP LENGTH DANIEL Z. LEVIN Management and Global Business Dept. Rutgers Business School Newark and New Brunswick Rutgers
More informationRunning head: CSP BOOK REVIEW 1
Running head: CSP BOOK REVIEW 1 Please use the following citation when referencing this work: McGill, R. J. (2013). Book review: Cognitive Therapy for Adolescents in School Settings. Contemporary School
More informationApproaches to Improving Causal Inference from Mediation Analysis
Approaches to Improving Causal Inference from Mediation Analysis David P. MacKinnon, Arizona State University Pennsylvania State University February 27, 2013 Background Traditional Mediation Methods Modern
More informationPanel: Using Structural Equation Modeling (SEM) Using Partial Least Squares (SmartPLS)
Panel: Using Structural Equation Modeling (SEM) Using Partial Least Squares (SmartPLS) Presenters: Dr. Faizan Ali, Assistant Professor Dr. Cihan Cobanoglu, McKibbon Endowed Chair Professor University of
More informationCSC2130: Empirical Research Methods for Software Engineering
CSC2130: Empirical Research Methods for Software Engineering Steve Easterbrook sme@cs.toronto.edu www.cs.toronto.edu/~sme/csc2130/ 2004-5 Steve Easterbrook. This presentation is available free for non-commercial
More informationSpeaker Notes: Qualitative Comparative Analysis (QCA) in Implementation Studies
Speaker Notes: Qualitative Comparative Analysis (QCA) in Implementation Studies PART 1: OVERVIEW Slide 1: Overview Welcome to Qualitative Comparative Analysis in Implementation Studies. This narrated powerpoint
More informationEuropean Federation of Statisticians in the Pharmaceutical Industry (EFSPI)
Page 1 of 14 European Federation of Statisticians in the Pharmaceutical Industry (EFSPI) COMMENTS ON DRAFT FDA Guidance for Industry - Non-Inferiority Clinical Trials Rapporteur: Bernhard Huitfeldt (bernhard.huitfeldt@astrazeneca.com)
More informationExperimental Psychology
Title Experimental Psychology Type Individual Document Map Authors Aristea Theodoropoulos, Patricia Sikorski Subject Social Studies Course None Selected Grade(s) 11, 12 Location Roxbury High School Curriculum
More informationInteraction Effects: Centering, Variance Inflation Factor, and Interpretation Issues
Robinson & Schumacker Interaction Effects: Centering, Variance Inflation Factor, and Interpretation Issues Cecil Robinson Randall E. Schumacker University of Alabama Research hypotheses that include interaction
More information26:010:557 / 26:620:557 Social Science Research Methods
26:010:557 / 26:620:557 Social Science Research Methods Dr. Peter R. Gillett Associate Professor Department of Accounting & Information Systems Rutgers Business School Newark & New Brunswick 1 Overview
More informationThe Role of Mediator in Relationship between Motivations with Learning Discipline of Student Academic Achievement
The Role of Mediator in Relationship between Motivations with Learning Discipline of Student Academic Achievement Zamri Chik, Abdul Hakim Abdullah To Link this Article: http://dx.doi.org/10.6007/ijarbss/v8-i11/4959
More informationTitle: Home Exposure to Arabian Incense (Bakhour) and Asthma Symptoms in Children: A Community Survey in Two Regions in Oman
Author's response to reviews Title: Home Exposure to Arabian Incense (Bakhour) and Asthma Symptoms in Children: A Community Survey in Two Regions in Oman Authors: Omar A Al-Rawas (orawas@squ.edu.om) Abdullah
More informationThe moderating impact of temporal separation on the association between intention and physical activity: a meta-analysis
PSYCHOLOGY, HEALTH & MEDICINE, 2016 VOL. 21, NO. 5, 625 631 http://dx.doi.org/10.1080/13548506.2015.1080371 The moderating impact of temporal separation on the association between intention and physical
More informationResearch Article Testing Theories of Dietary Behavior Change in Youth Using the Mediating Variable Model with Intervention Programs
Research Article Testing Theories of Dietary Behavior Change in Youth Using the Mediating Variable Model with Intervention Programs Continuing Education Questionnaire available at www.sne.org/ Meets Learning
More informationDiscussion of: Koch & Salterio, Pressures on Audit Partners Negotiation Strategy and Decision Making
Discussion of: Koch & Salterio, Pressures on Audit Partners Negotiation Strategy and Decision Making Illinois Audit Symposium September 12, 2014 Brian White My first reaction to this paper We know auditors
More informationEvaluation Models STUDIES OF DIAGNOSTIC EFFICIENCY
2. Evaluation Model 2 Evaluation Models To understand the strengths and weaknesses of evaluation, one must keep in mind its fundamental purpose: to inform those who make decisions. The inferences drawn
More informationEmpowered by Psychometrics The Fundamentals of Psychometrics. Jim Wollack University of Wisconsin Madison
Empowered by Psychometrics The Fundamentals of Psychometrics Jim Wollack University of Wisconsin Madison Psycho-what? Psychometrics is the field of study concerned with the measurement of mental and psychological
More information12/31/2016. PSY 512: Advanced Statistics for Psychological and Behavioral Research 2
PSY 512: Advanced Statistics for Psychological and Behavioral Research 2 Introduce moderated multiple regression Continuous predictor continuous predictor Continuous predictor categorical predictor Understand
More informationCausal Inference in Randomized Experiments With Mediational Processes
Psychological Methods 2008, Vol. 13, No. 4, 314 336 Copyright 2008 by the American Psychological Association 1082-989X/08/$12.00 DOI: 10.1037/a0014207 Causal Inference in Randomized Experiments With Mediational
More informationBasic concepts and principles of classical test theory
Basic concepts and principles of classical test theory Jan-Eric Gustafsson What is measurement? Assignment of numbers to aspects of individuals according to some rule. The aspect which is measured must
More informationCritical Thinking Assessment at MCC. How are we doing?
Critical Thinking Assessment at MCC How are we doing? Prepared by Maura McCool, M.S. Office of Research, Evaluation and Assessment Metropolitan Community Colleges Fall 2003 1 General Education Assessment
More informationCorrelation and Regression
Dublin Institute of Technology ARROW@DIT Books/Book Chapters School of Management 2012-10 Correlation and Regression Donal O'Brien Dublin Institute of Technology, donal.obrien@dit.ie Pamela Sharkey Scott
More informationBOOTSTRAPPING CONFIDENCE LEVELS FOR HYPOTHESES ABOUT REGRESSION MODELS
BOOTSTRAPPING CONFIDENCE LEVELS FOR HYPOTHESES ABOUT REGRESSION MODELS 17 December 2009 Michael Wood University of Portsmouth Business School SBS Department, Richmond Building Portland Street, Portsmouth
More informationClinical Trials A Practical Guide to Design, Analysis, and Reporting
Clinical Trials A Practical Guide to Design, Analysis, and Reporting Duolao Wang, PhD Ameet Bakhai, MBBS, MRCP Statistician Cardiologist Clinical Trials A Practical Guide to Design, Analysis, and Reporting
More informationWhen the Evidence Says, Yes, No, and Maybe So
CURRENT DIRECTIONS IN PSYCHOLOGICAL SCIENCE When the Evidence Says, Yes, No, and Maybe So Attending to and Interpreting Inconsistent Findings Among Evidence-Based Interventions Yale University ABSTRACT
More informationRussian Journal of Agricultural and Socio-Economic Sciences, 3(15)
ON THE COMPARISON OF BAYESIAN INFORMATION CRITERION AND DRAPER S INFORMATION CRITERION IN SELECTION OF AN ASYMMETRIC PRICE RELATIONSHIP: BOOTSTRAP SIMULATION RESULTS Henry de-graft Acquah, Senior Lecturer
More informationRegression Discontinuity Analysis
Regression Discontinuity Analysis A researcher wants to determine whether tutoring underachieving middle school students improves their math grades. Another wonders whether providing financial aid to low-income
More informationTwo-Condition Within-Participant Statistical Mediation Analysis: A Path-Analytic Framework
Psychological Methods 2016 American Psychological Association 2017, Vol. 22, No. 1, 6 27 1082-989X/17/$12.00 http://dx.doi.org/10.1037/met0000086 Two-Condition Within-Participant Statistical Mediation
More informationcommentary Time is a jailer: what do alpha and its alternatives tell us about reliability?
commentary Time is a jailer: what do alpha and its alternatives tell us about reliability? Rik Psychologists do not have it easy, but the article by Peters Maastricht University (2014) paves the way for
More informationEstimating Reliability in Primary Research. Michael T. Brannick. University of South Florida
Estimating Reliability 1 Running Head: ESTIMATING RELIABILITY Estimating Reliability in Primary Research Michael T. Brannick University of South Florida Paper presented in S. Morris (Chair) Advances in
More informationDefining Evidence-Based : Developing a Standard for Judging the Quality of Evidence for Program Effectiveness and Utility
Defining Evidence-Based : Developing a Standard for Judging the Quality of Evidence for Program Effectiveness and Utility Delbert S. Elliott Center for the Study and Prevention of Violence University of
More informationMeta-Analysis and Subgroups
Prev Sci (2013) 14:134 143 DOI 10.1007/s11121-013-0377-7 Meta-Analysis and Subgroups Michael Borenstein & Julian P. T. Higgins Published online: 13 March 2013 # Society for Prevention Research 2013 Abstract
More informationPERCEPTUAL BODY DISTORTION AND BODY DISSATISFACTION: A STUDY USING ADJUSTABLE PARTIAL IMAGE DISTORTION
PERCEPTUAL BODY DISTORTION AND BODY DISSATISFACTION: A STUDY USING ADJUSTABLE PARTIAL IMAGE DISTORTION Davide Massidda, Alessia Bastianelli, Giulio Vidotto Department of General Psychology, University
More informationLecture 9 Internal Validity
Lecture 9 Internal Validity Objectives Internal Validity Threats to Internal Validity Causality Bayesian Networks Internal validity The extent to which the hypothesized relationship between 2 or more variables
More information